Hough transform network: Learning conoidal structures in a connectionist framework

A two-layer neural-network model is designed which accepts image coordinates as the input and learns the parametric form of conoidal shapes (lines/circles/ellipses) adaptively. It provides an efficient representation of visual information embedded in the connection weights and the parameters of the processing elements. It not only reduces the large space requirements of classical Hough transform (HT), but also represents parameters with a higher precision. The performance of the methodology is compared with other existing algorithms and has been found to excel over those algorithms in many cases.